The DOI matches its Crossref record, with a small difference in title or year; 31 of the 31 references it cites were found in a registry or an index; 2 of the 14 sources its claims cite were checked for support.
ImageNet classification with deep convolutional neural networks
Open accessAbstract
We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes. On the test data, we achieved top-1 and top-5 error rates of 37.5% and 17.0%, respectively, which is considerably better than the previous state-of-the-art. The neural network, which has 60 million parameters and 650,000 neurons, consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully connected layers with a final 1000-way softmax. To make training faster, we used non-saturating neurons and a very efficient GPU implementation of the convolution operation. To reduce overfitting in the fully connected layers we employed a recently developed regularization method called "dropout" that proved to be very effective. We also entered a variant of this model in the ILSVRC-2012 competition and achieved a winning top-5 test error rate of 15.3%, compared to 26.2% achieved by the second-best entry.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
42 Own result · 21 Own interpretation · 7 No basis given · 4 No full text of the sources · 3 Sources not found · 2 Supported · 2 Not checked yet · 1 Own method
Every source the paper cites for this claim was checked and supports it.
In the paper
“This technique reduces complex co adaptations of neurons, since a neuron cannot rely on the presence of particular other neurons.”
§ ImageNet Classification with Deep Convolutional Neural Networks > 5.2. Dropout
What the sources say
- Supportedcites [12] 12. Hinton, G., Srivastava, N., Krizhevsky, A., Sutskever, I., Salakhutdinov, R. Improving neural networks by preventing co-adaptation of feature detectors. arXiv preprint arXiv:1207.0580 (2012). source
The paper states on p. 1 that dropout prevents complex co-adaptations by randomly omitting hidden units, so a hidden unit cannot rely on other hidden units being present. This directly supports the citing sentence. (LLM, conf=0.95)
plainstates a cause- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“There is, however, a very efficient version of model combination that only costs about a factor of two during training.”
§ ImageNet Classification with Deep Convolutional Neural Networks > 5.2. Dropout
What the sources say
- Supportedcites [12] 12. Hinton, G., Srivastava, N., Krizhevsky, A., Sutskever, I., Salakhutdinov, R. Improving neural networks by preventing co-adaptation of feature detectors. arXiv preprint arXiv:1207.0580 (2012). source
The paper describes dropout as an efficient way to combine many neural-network models (p. 1). In F.6 Training (p. 17), it reports that ImageNet training took roughly four days with dropout versus two days without it, supporting the approximate factor-of-two training cost. (LLM, conf=0.88)
plainstates a cause- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Supported
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This scheme reduces our top-1 and top-5 error rates by 1.7% and 1.2%, respectively, as compared with a net with half as many kernels in each convolutional layer trained on one GPU.”
§ ImageNet Classification with Deep Convolutional Neural Networks > 4.2. Training on multiple GPUs
plainstates a cause- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper states — it cites no source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The size of our network made overfitting a significant problem, even with 1.2 million labeled training examples”
§ ImageNet Classification with Deep Convolutional Neural Networks > 2. INTRODUCTION
plainstates a cause- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper states — it cites no source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“So these data augmentation schemes are, in effect, computationally free.”
§ ImageNet Classification with Deep Convolutional Neural Networks > 5.1. Data augmentation
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- It may state two separate claims as one. 18
- Probably not a substantial claim. 26
- The quote only partly states this claim. 2
Create a free account to see these claims.
Create a free account to see these assumptions.
34 more claims
Create a free account to see all 82 claims and to verify the 3 references still open.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“In the field of DL, the CNN is the most famous and commonly employed algorithm [30, 71–75].”
From Review of deep learning: concepts, CNN architectures, challenges, applications, future directions (2021) · cites this paper as [30]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Convolutional neural networks (CNNs), built for spatial data, have demonstrated performance comparable to that of specialists in cancer detection and tumor segmentation [15,16,17,18,19,20].”
From Artificial Intelligence in Clinical Oncology: From Productivity Enhancement to Creative Discovery (2025) · cites this paper as [16]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Deep CNN architectures with 8, even 22 layers [4], [33], can be useful even for CADe problems where the available training datasets are limited.”
From Deep convolutional neural networks for computer-aided detection: cnn architectures, dataset characteristics and transfer learning (2016) · cites this paper as [4]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Fully convolutional neural networks (fCNNs) have provided state-of-the-art performance for object classification and image segmentation (1).”
From Fully automated, real-time 3D ultrasound segmentation to estimate first trimester placental volume using deep learning (2018) · cites this paper as [1]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“The first wave saw the emergence of deep learning as the dominant paradigm for analyzing high-dimensional biological data.”
From The next paradigm in bioinformatics: a review of multi-agent systems and foundational models for end-to-end scientific discovery (2026) · cites this paper as [1]
Experimental — accuracy not yet measured
19 more claims about this paper
Create a free account to see every claim other papers make about this one.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ ImageNet Classification with Deep Convolutional Neural Networks > 2. INTRODUCTION
“we found that removing any convolutional layer (each of which contains no more than 1% of the model's parameters) resulted in inferior performance.”
§ ImageNet Classification with Deep Convolutional Neural Networks > 4.5. Overall architecture
“The third convolutional layer has 384 kernels of size 3 × 3 × 256”
Citations
Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333
Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298594 · Cited by 48,562
Learning Multiple Layers of Features from Tiny Images
Krizhevsky, A. (2024) · doi:10.57702/zp44cu3g · Cited by 23,884
Self-citationLearning Internal Representations by Error Propagation
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1985) · doi:10.21236/ada164453 · Cited by 16,501
Self-citation(2010) · International Conference on Machine Learning · Cited by 13,995
Self-citationImproving neural networks by preventing co-adaptation of feature detectors
Hinton, G. E., Srivastava, N., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. R. (2012) · arXiv · doi:10.48550/arxiv.1207.0580 · Cited by 6,382
Self-citationFukushima, K. (1980) · Biological Cybernetics · doi:10.1007/bf00344251 · Cited by 5,759
LabelMe: A Database and Web-Based Tool for Image Annotation
Russell, B. C., Torralba, A., Murphy, K. P., & Freeman, W. T. (2008) · International Journal of Computer Vision · doi:10.1007/s11263-007-0090-8 · Cited by 4,210
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2015) · arXiv · doi:10.48550/arxiv.1512.03385 · Cited by 4,196
Research works printed but not linked: 13
- Berg A., 2010, Large scale visual recognition challenge
- Deng J., 2009, CVPR09
- Krizhevsky A., 2009, Department of Computer Science
- Krizhevsky A., 2010, Convolutional deep belief networks on cifar-10. Unpublished manuscript
- Krizhevsky A., 2011, ESANN
- LeCun Y., 1990, Advances in Neural Information Processing Systems
- LeCun Y., 1985, Une procedure d'apprentissage pour reseau a seuil asymmetrique (a learning scheme for asymmetric threshold networks)
- 10.5555/1896300.1896315 · DOI
- Mensink T., 2012, Italy
- 10.5555/3104322.3104425 · DOI
- Rumelhart D.E., 1985, DTIC Document
- 10.5555/938980.939477 · DOI
- Werbos P., 1974, Beyond regression: New tools for prediction and analysis in the behavioral sciences
Cited by
Items 53,901–54,000 of 108,439
Pedestrian instance segmentation with prior structure of semantic parts
Chu, H., Ma, H., & Li, X. (2021) · Pattern Recognition Letters · doi:10.1016/j.patrec.2021.05.012 · Cited by 9
Chen, H.-Y., & Lin, T.-N. (2021) · IEEE Access · doi:10.1109/access.2021.3087528 · Cited by 9
Video target detection of East Asian migratory locust based on the MOG2-YOLOv4 network
Bai, Z., Tang, Z., Diao, L., Lu, S., Guo, X., Zhou, H., Liu, C., & Lin, L. (2021) · International Journal of Tropical Insect Science · doi:10.1007/s42690-021-00602-8 · Cited by 10
Wang, X., Chen, A., Zhang, L., Gu, Y., Xu, M., & Yan, H. (2021) · Wireless Communications and Mobile Computing · doi:10.1155/2021/4319074 · Cited by 9
A Single Target Grasp Detection Network Based on Convolutional Neural Network
Zhang, L., & Wu, D. (2021) · Computational Intelligence and Neuroscience · doi:10.1155/2021/5512728 · Cited by 9
Utilizing geo-referenced imagery for systematic social observation of neighborhood disorder
Snaphaan, T., & Hardyns, W. (2021) · Computers Environment and Urban Systems · doi:10.1016/j.compenvurbsys.2021.101691 · Cited by 18
Zhao, L., Wang, Y., Duan, Z., Chen, D., & Liu, S. (2021) · IEEE Access · doi:10.1109/access.2021.3097054 · Cited by 8
Activity guided multi-scales collaboration based on scaled-CNN for saliency prediction
Cheng, D., Liu, R., Li, J., et al. (2021) · Image and Vision Computing · doi:10.1016/j.imavis.2021.104267 · Cited by 9
FCA-BNN: Flexible and Configurable Accelerator for Binarized Neural Networks on FPGA
Gao, J., Yao, Y., Li, Z., et al. (2021) · IEICE Transactions on Information and Systems · doi:10.1587/transinf.2021edp7054 · Cited by 9
Vijayan, V., Joy, C. M., & Shailesh, S. (2021) · International Conference on Communication, Control and Information Sciences (ICCISc) · doi:10.1109/iccisc52257.2021.9484914 · Cited by 9
Automated Framework to Translate Rebar Spatial Information from GPR into BIM
Xiang, Z., Rashidi, A., & Ou, G. (2021) · Journal of Construction Engineering and Management · doi:10.1061/(asce)co.1943-7862.0002141 · Cited by 9
Hateful meme detection with multimodal deep neural networks
Constantin, M. G., Pârvu, D.-Ș., Stanciu, C. L., Ionaşcu, D., & Ionescu, B. E. (2021) · International Symposium on Signals, Circuits and Systems · doi:10.1109/isscs52333.2021.9497374 · Cited by 9
Wang, C., Ge, S., Jiang, Z., Hao, H., & Gu, Q. (2021) · Computers & Geosciences · doi:10.1016/j.cageo.2021.104912 · Cited by 9
Lightweight Connectivity In Graph Convolutional Networks For Skeleton-Based Recognition
Sahbi, H. (2021) · Proceedings - International Conference on Image Processing · doi:10.1109/icip42928.2021.9506774 · Cited by 11
Gan-Based Super-Resolution and Segmentation of Retinal Layers in Optical Coherence Tomography Scans
Jeihouni, P., Dehzangi, O., Amireskandari, A., et al. (2021) · Proceedings - International Conference on Image Processing · doi:10.1109/icip42928.2021.9506291 · Cited by 9
A statistical framework for evaluating convolutional neural networks. Application to colon cancer
Popa, L. (2021) · Annals of the University of Craiova Mathematics and Computer Science Series · doi:10.52846/ami.v48i1.1449 · Cited by 9
Periocular Recognition using CNN based Feature Extraction and Classification
Kayande, V., Kaware, P., Khambete, A., et al. (2021) · IEEE International Conference on Communication Systems and Network Technologies (CSNT) · doi:10.1109/csnt51715.2021.9509734 · Cited by 9
Resource and Performance Estimation for CNN Models using Machine Learning
Shahshahani, M., & Bhatia, D. K. (2021) · IEEE Computer Society Annual Symposium on VLSI · doi:10.1109/isvlsi51109.2021.00019 · Cited by 9
Learning-based extraction of first-order logic representations of API directives
Liu, M., Peng, X., Marcus, A., Treude, C., Bai, X., Lyu, G., Xie, J., & Zhang, X. (2021) · ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE) · doi:10.1145/3468264.3468618 · Cited by 8
An Optimized Approach to Vehicle-Type Classification Using a Convolutional Neural Network
Habib, S., & Khan, N. F. (2021) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2021.015504 · Cited by 9
Optimal Training Configurations of a CNN-LSTM-Based Tracker for a Fall Frame Detection System
Mohamed, N. A., Zulkifley, M. A., Ibrahim, A. A., & Mustapha, A. (2021) · Sensors · doi:10.3390/s21196485 · Cited by 10
Deep Neural Networks for Melanoma Detection from Optical Standard Images using Transfer Learning
Abbes, W., & Sellami, D. (2021) · Procedia Computer Science · doi:10.1016/j.procs.2021.08.134 · Cited by 9
Verzat, C., Harley, J., Patani, R., & Luisier, R. (2021) · Neuropathology and Applied Neurobiology · doi:10.1111/nan.12770 · Cited by 10
Parallel-Structure Deep Learning for Prediction of Remaining Time of Process Instances
Wahid, N. A., Bae, H., Adi, T. N., Choi, Y.-L., & Iskandar, Y. A. (2021) · Applied Sciences · doi:10.3390/app11219848 · Cited by 10
Vision-Based Pointing Estimation and Evaluation in Toddlers for Autism Screening
Qin, H., Wang, Z., Liu, J., et al. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-89134-3_17 · Cited by 9
Yang, S., Luo, X., & Li, C. (2021) · Strojniški vestnik – Journal of Mechanical Engineering · doi:10.5545/sv-jme.2021.7284 · Cited by 10
Wheat kernels classification using visible-near infrared camera based on deep learning
Özkan, K., Seke, E., & Işık, Ş. (2021) · Pamukkale University Journal of Engineering Sciences · doi:10.5505/pajes.2020.80774 · Cited by 9
Hidden in plain sight? Irregularities on statutory forms and electoral fraud
Warner, Z., Harris, J. A., Brown, M., & Arnold, C. (2021) · Electoral Studies · doi:10.1016/j.electstud.2021.102411 · Cited by 9
Hu, Z., Kou, G., Zhang, H., Li, N., Yang, K., & Liu, L. (2021) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3459637.3482469 · Cited by 11
Yin, Q., Li, J., Ma, F., et al. (2021) · Remote Sensing · doi:10.3390/rs13224503 · Cited by 9
Image Data Augmentation techniques for Deep Learning -A Mirror Review
Saini, D., & Malik, R. (2021) · International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO) · doi:10.1109/icrito51393.2021.9596262 · Cited by 9
TSInsight: A Local-Global Attribution Framework for Interpretability in Time Series Data
Siddiqui, S. A., Mercier, D., Dengel, A. R., et al. (2021) · Sensors · doi:10.3390/s21217373 · Cited by 9
Liu, H., Xu, Y., Wang, C., Ding, F., & Xiao, H. (2021) · Materials Research Express · doi:10.1088/2053-1591/ac3a40 · Cited by 9
Artificial Intelligence and Cardiac PET/Computed Tomography Imaging
Miller, R. J. H., Singh, A., Dey, D., & Slomka, P. J. (2021) · PET Clinics · doi:10.1016/j.cpet.2021.06.011 · Cited by 9
Systematic Literature Review: An Intelligent Pulmonary TB Detection from Chest X-Rays
Jimmy, Cenggoro, T. W., & Pardamean, B. (2021) · doi:10.1109/iccsai53272.2021.9609717 · Cited by 9
Skin Pathology Detection Using Artificial Intelligence
Vadupu, S., Kandala, K. S., Peddi, A., et al. (2021) · International Conference on Signal Processing, Computing and Control (ISPCC) · doi:10.1109/ispcc53510.2021.9609516 · Cited by 9
Evaluation of automated airway morphological quantification for assessing fibrosing lung disease
Pakzad, A., Cheung, W. K., Moorsel, C. V., et al. (2024) · Computer Methods in Biomechanics and Biomedical Engineering Imaging & Visualization · doi:10.1080/21681163.2024.2325361 · Cited by 9
Uzunidis, D., Stavdas, A., Kasnesis, P., et al. (2021) · European Conference on Optical Communication · doi:10.1109/ecoc52684.2021.9605893 · Cited by 9
A deep learning-based ring artifact correction method for X-ray CT
Yuan, L., Xu, Q., Liu, B., et al. (2021) · Radiation Detection Technology and Methods · doi:10.1007/s41605-021-00286-1 · Cited by 9
Dynamic Parallel Pyramid Networks for Scene Recognition
Liu, K., & Moon, S. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3129227 · Cited by 13
Vision Transformer-Based Tailing Detection in Videos
Lee, J., Lee, S., Cho, W., Siddiqui, Z. A., & Park, U. (2021) · Applied Sciences · doi:10.3390/app112411591 · Cited by 11
Research on Identification of Corn Disease Occurrence Degree Based on Improved ResNeXt Network
Wang, G., Wang, J., Yu, H., et al. (2021) · International Journal of Pattern Recognition and Artificial Intelligence · doi:10.1142/s0218001422500057 · Cited by 9
Towards an automatic characterization of riverscape development by deep learning
Alfredsen, K. T., Dalsgård, A., Shamsaliei, S., Halleraker, J. H., & Gundersen, O. E. (2021) · River Research and Applications · doi:10.1002/rra.3927 · Cited by 10
Tuncer, T., Doğan, Ş., Akbal, E., et al. (2022) · Neural Computing and Applications · doi:10.1007/s00521-021-06678-0 · Cited by 9
CMD: controllable matrix decomposition with global optimization for deep neural network compression
Zhang, H., Liu, L., Zhou, H., Sun, H., & Zheng, N. (2022) · Machine Learning · doi:10.1007/s10994-021-06077-5 · Cited by 9
Takayanagi, Y., Takayama, Y., Iijima, K., et al. (2021) · Advanced Biomedical Engineering · doi:10.14326/abe.10.158 · Cited by 9
LPI Radar Waveform Recognition Based on Neural Architecture Search
Ma, Z., Yu, W., Zhang, P., Huang, Z. G., Lin, A., & Xia, Y. (2022) · Computational Intelligence and Neuroscience · doi:10.1155/2022/4628481 · Cited by 9
Usamentiaga, R., Lema, D. G., Pedrayes, O. D., & Garcia, D. F. (2021) · IEEE Industry Applications Society Annual Meeting (IAS) · doi:10.1109/ias48185.2021.9677231 · Cited by 9
Document Image Classification Method Based on Graph Convolutional Network
Xiong, Y., Dai, Z., Liu, Y., et al. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-92185-9_26 · Cited by 9
Research and Implementation of Forest Fire Smoke Detection Based on ResNet Transfer Learning
Lv, H. P., & Chen, X. (2021) · International Conference on Electronic Information Technology and Computer Engineering (EITCE) · doi:10.1145/3501409.3501523 · Cited by 9
Khadim, E. U., Shah, S. A., & Wagan, R. A. (2021) · International Conference on Innovative Computing (ICIC) · doi:10.1109/icic53490.2021.9693056 · Cited by 9
Li, S., Si, P., Zhang, Z., Zhu, J., He, X., & Zhang, N. (2022) · Journal of Medical and Biological Engineering · doi:10.1007/s40846-022-00689-5 · Cited by 10
Wood species recognition from wood images with an improved CNN1
Miao, Y., Zhu, S., Hua, H., et al. (2022) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-211097 · Cited by 9
Aspect-Based Sentiment Analysis Using Interaction Matrix And Global Attention Neural Network
Wang, X., Pan, X., Yang, T., Xie, J., & Tang, M. (2022) · The Computer Journal · doi:10.1093/comjnl/bxac005 · Cited by 10
Car engine sounds recognition based on deformable feature map residual network
Wu, Z., Wan, Z., Ge, D., et al. (2022) · Scientific Reports · doi:10.1038/s41598-022-06818-z · Cited by 9
Comparative study of 3D object detection frameworks based on LiDAR data and sensor fusion techniques
Venugopala, S. H. (2022) · Journal of Physics Conference Series · doi:10.1088/1742-6596/2232/1/012015 · Cited by 9
A Study of Split Learning Model
Ryu, J., Won, D., & Lee, Y. (2022) · International Conference on Ubiquitous Information Management and Communication (IMCOM) · doi:10.1109/imcom53663.2022.9721798 · Cited by 14
Unequal adaptive visual recognition by learning from multi-modal data
Cai, Z., Zhang, T., Jing, X., & Shao, L. (2022) · Information Sciences · doi:10.1016/j.ins.2022.03.076 · Cited by 9
PrivPAS: A real time Privacy-Preserving AI System and applied ethics
Harichandana, B. S. S., Agarwal, V., Ghosh, S. K., Ramena, G., Kumar, S., & Raja, B. R. K. (2022) · IEEE International Conference on Semantic Computing (ICSC) · doi:10.1109/icsc52841.2022.00010 · Cited by 9
Explainable gait recognition with prototyping encoder–decoder
Moon, J., Shin, Y.-M., Park, J.-D., Minaya, N. H., Shin, W.-Y., & Choi, S. (2022) · PLoS ONE · doi:10.1371/journal.pone.0264783 · Cited by 11
Budak, C., & Mençik, V. (2022) · Neural Computing and Applications · doi:10.1007/s00521-022-07183-8 · Cited by 9
Annual dilated convolution neural network for newbuilding ship prices forecasting
Gao, R., Liu, J., Bai, X., et al. (2022) · Neural Computing and Applications · doi:10.1007/s00521-022-07075-x · Cited by 9
Njirjak, M., Otović, E., Jozinović, D., Lerga, J., Mauša, G., Michelini, A., & Štajduhar, I. (2022) · Mathematics · doi:10.3390/math10060965 · Cited by 9
Detection of Face Morphing Attacks Based on Patch-Level Features and Lightweight Networks
Long, M., Zhao, X., Zhang, L.-B., & Peng, F. (2022) · Security and Communication Networks · doi:10.1155/2022/7460330 · Cited by 12
Vesala, G. T., Ghali, V. S., Subhani, S., et al. (2022) · SN Computer Science · doi:10.1007/s42979-022-01055-7 · Cited by 9
Adaptive Pseudo Labeling for Source-Free Domain Adaptation in Medical Image Segmentation
Li, C., Chen, W., Luo, X., et al. (2022) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp43922.2022.9746286 · Cited by 9
Convolutional Neural Network-Based Radar Antenna Scanning Period Recognition
Wang, B., Wang, S., Zeng, D., & Wang, M. (2022) · Electronics · doi:10.3390/electronics11091383 · Cited by 10
Design of a Real-Time Corrosion Detection and Quantification Protocol for Automobiles
Dhonde, K., Mirhassani, M., Tam, E. K. L., et al. (2022) · Materials · doi:10.3390/ma15093211 · Cited by 9
Fake Data Injection Attack Detection in AMI System Using a Hybrid Method
Na, L., Xiaohui, X., Xiaoqin, M., et al. (2021) · IEEE Sustainable Power and Energy Conference (iSPEC) · doi:10.1109/ispec53008.2021.9735875 · Cited by 9
MLP-Pose: Human Pose Estimation by MLP-Mixer
Xiong, S., Qu, Z., Wang, Y., et al. (2021) · IEEE International Conference on Cloud Computing and Intelligent Systems (CCIS) · doi:10.1109/ccis53392.2021.9754658 · Cited by 9
Fu, M., Duan, Y., Cheng, Z., Qin, W., Wang, Y., Liang, D., & Hu, Z. (2022) · Medical Physics · doi:10.1002/mp.16163 · Cited by 10
The Effectiveness of Image Augmentation in Breast Cancer Type Classification Using Deep Learning
Li, Z., & Wu, Y. (2021) · International Conference on Machine Learning, Big Data and Business Intelligence (MLBDBI) · doi:10.1109/mlbdbi54094.2021.00134 · Cited by 9
Nakayama, L., Ribeiro, L. Z., Malerbi, F., & Regatieri, C. (2022) · Frontiers in Ophthalmology · doi:10.3389/fopht.2022.898181 · Cited by 8
RAISE: Rank-Aware Incremental Learning for Remote Sensing Object Detection
Li, H., Chen, Y., Zhang, Z., & Peng, J. (2022) · Symmetry · doi:10.3390/sym14051020 · Cited by 9
Damage Detection in Wind Turbine Blades Based on an Improved Broad Learning System Model
Zou, L., Wang, Y., Bi, J., & Sun, Y. (2022) · Applied Sciences · doi:10.3390/app12105164 · Cited by 10
Shen, Y. (2022) · Scientific Programming · doi:10.1155/2022/2211468 · Cited by 9
Human Pose Estimation Based on Lite HRNet with Coordinate Attention
Li, R., Huang, H., & Zheng, Y. (2022) · International Conference on Intelligent Computing and Signal Processing (ICSP) · doi:10.1109/icsp54964.2022.9778346 · Cited by 9
Jing, Z., Luo, Y., Li, X., & Xu, X. (2022) · Industrial Management & Data Systems · doi:10.1108/imds-01-2022-0020 · Cited by 9
Multi-Conditional Constraint Generative Adversarial Network-Based MR Imaging from CT Scan Data
Liu, M., Zou, W., Wang, W., et al. (2022) · Sensors · doi:10.3390/s22114043 · Cited by 9
Crack Detection based on Convnext and Normalization
Zhang, H., Liu, C., Ho, J., et al. (2022) · Journal of Physics Conference Series · doi:10.1088/1742-6596/2289/1/012022 · Cited by 9
Deformation Adjustment with Single Real Signature Image for Biometric Verification Using CNN
Kumar, R., Saraswat, M., Ather, D., Bhutta, M. N. M., Basheer, S., & Thakur, R. N. (2022) · Computational Intelligence and Neuroscience · doi:10.1155/2022/4406101 · Cited by 10
RetractedAlowolodu, O. D., Adetunmbi, A. O., Mebawondu, J. O., & Mebawondu, O. J. (2022) · IEEE Nigeria International Conference on Disruptive Technologies for Sustainable Development (NIGERCON) · doi:10.1109/nigercon54645.2022.9803080 · Cited by 10
Alagarsamy, M., Anbalagan, K., Thangavel, Y., Sakkarai, J., Pauliah, J., & Kannadhasan, S. (2022) · Bulletin of Electrical Engineering and Informatics · doi:10.11591/eei.v11i4.3290 · Cited by 9
Wang, Y., & Birch, D. G. (2022) · Frontiers in Medicine · doi:10.3389/fmed.2022.932498 · Cited by 9
SBNN: A Searched Binary Neural Network for SAR Ship Classification
Zhu, H., Guo, S., Sheng, W., & Xiao, L. (2022) · Applied Sciences · doi:10.3390/app12146866 · Cited by 9
Lightweight Encoder-Decoder Architecture for Foot Ulcer Segmentation
Ali, S., Mahmood, A., & Jung, S. K. (2022) · Communications in computer and information science · doi:10.1007/978-3-031-06381-7_17 · Cited by 9
An update on the knee osteoarthritis severity grading using wide residual learning
Helwan, A., Azar, D., & Abdellatef, H. (2022) · Journal of X-Ray Science and Technology · doi:10.3233/xst-221190 · Cited by 9
A deep learning-based approach for mushroom diseases classification
Zahan, N., Hasan, M. Z., Uddin, M. S., Hossain, S., & Islam, S. F. (2022) · Elsevier eBooks · doi:10.1016/b978-0-323-90550-3.00005-9 · Cited by 9
Kim, H., & Lee, S. H. (2022) · IEEE Access · doi:10.1109/access.2022.3186336 · Cited by 9
Aircarft Signal Feature Extraction and Recognition Based on Deep Learning
Wang, G., Zou, C., Zhang, C., Pan, C., Song, J., & Yang, F. C. (2022) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2022.3180483 · Cited by 10
Liang, S., Zhan, Z., Yao, F., Cheng, L., & Zhang, Z. (2022) · IEEE Security and Privacy Workshops (SPW) · doi:10.1109/spw54247.2022.9833894 · Cited by 11
A review of feature fusion-based media popularity prediction methods
Liu, A.-A., Wang, X., Xu, N., Guo, J., Jin, G., Zhang, Q., Tang, Y., & Zhang, S. (2022) · Visual Informatics · doi:10.1016/j.visinf.2022.07.003 · Cited by 9
Guo, W., Liu, S., Gong, Z., Zhang, G., & Jiang, X. (2022) · International Journal of Imaging Systems and Technology · doi:10.1002/ima.22790 · Cited by 9
Chen, H., Huang, H., Zuo, X., & Zhao, X. (2022) · Mathematics · doi:10.3390/math10152724 · Cited by 9
Xing, H., Zhang, X., Nie, Y., Wang, S., Wang, T., Jing, H., & Li, F. (2022) · Quantitative Imaging in Medicine and Surgery · doi:10.21037/qims-21-1116 · Cited by 8
The Construction of English Smart Classroom Teaching Mode Based on Deep Learning
Niu, J., & Liu, Y. (2022) · Computational Intelligence and Neuroscience · doi:10.1155/2022/9037010 · Cited by 12
Linear Combination Approximation of Feature for Channel Pruning
Joo, D., Kim, D., Yi, E., & Kim, J. (2022) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw56347.2022.00313 · Cited by 10
Contactless Fall Detection by Means of Multiple Bioradars and Transfer Learning
Lobanova, V., Slizov, V., & Anishchenko, L. N. (2022) · Sensors · doi:10.3390/s22166285 · Cited by 9
Cassava plant disease detection with imbalanced dataset using transfer learning
Yadav, R., Pandey, M., & Sahu, S. K. (2022) · IEEE World Conference on Applied Intelligence and Computing (AIC) · doi:10.1109/aic55036.2022.9848882 · Cited by 9
Zhang, S., Zhang, X., Li, T., Meng, H., Cao, X., & Wang, L. (2022) · Remote Sensing · doi:10.3390/rs14112612 · Cited by 9
Metadata
- Authors
- Krizhevsky, A., Sutskever, I., & Hinton, G. E.
- Affiliations
- Google (United States) (US)
- University of Toronto (CA)
- Venue
- Communications of the ACM
- Published
- 2017-05-24
- Volume
- 60
- Issue
- 6
- Pages
- 84-90
- ISSN
- 0001-0782 (ISSN-L)
- 1557-7317
- OpenAlex
- W2163605009
- Cited by
- 112,155
- References
- 27
- Citations by year
- 2026: 2710 · 2025: 7710 · 2024: 10211 · 2023: 12267 · 2022: 12280 · 2021: 15497 · 2020: 14538 · 2019: 13976 · 2018: 9999 · 2017: 6661
- FWCI
- 5127.18
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Communications of the ACM (publishedVersion)
- neural information processing systems
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Keywords
- ImageNet, image classification, deep convolutional neural networks, dropout regularization, overfitting
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-10-11, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-11 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Cited sources are checked with a language model’s help (gpt-6-sol).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
